Moving object extraction methods are traditionally used in video compression techniques to extract a contour of a moving object in a video sequence. Traditional methods often explicitly introduce a model to extract the contour. These traditional methods can have significant problems such as discretization of the contour and difficulty controlling the length and curvature as the contour evolves.
For example, simple segmentation of a motion block can be performed to capture multiple moving objects so as to reduce the prediction error. This process can be achieved by using a quadtree segmentation of a block having a large prediction error into sub-blocks for improved motion estimation. The block having the large prediction error is typically quadtree segmented using a straight line model of the moving object's boundary.
Other approaches in motion segmentation rely on optical flow estimates or parametric (i.e., affine) motion models. These approaches have problems, such as occlusion effects, near object boundaries. Some degree of smoothness in the segmentation field, and hence in object boundaries, can be achieved using MAP/Bayesian methods, which include a prior probability term. These methods constrain the connectivity of the segmentation field without any explicitly coupled model to account for the object boundary and motion fields.
In some conventional approaches, a curvature evolution model is used to capture the moving object boundary. However, these approaches do not involve motion estimations, and they rely only on a temporal difference operator in the model for object boundary evolution.
There is a need for a moving object extraction method that performs a region competition so as to grow the object from an initial condition, and to reach a state that provides a balance among prediction error reduction, boundary stability (i.e., no holes in the object, and a smoothness to the contour), and a coupling to image features.